2026-09-20T00:39:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/179812022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
James B. Orlin and Nitin Patel.
Thirathon, Nattavude, 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-06-02T19:29:58Z
2005-06-02T19:29:58Z
2004
2004
http://hdl.handle.net/1721.1/17981
57189284
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 151-152).
Cyclic Exchange is an application of the cyclic transfers neighborhood search technique for the k-means clustering problem. Neighbors of a feasible solution are obtained by moving points between clusters in a cycle. This method attempts to improve local minima obtained by the well-known Lloyd's algorithm. Although the results did not establish usefulness of Cyclic Exchange, our experiments reveal some insights on the k-means clustering and Lloyd's algorithm. While Lloyd's algorithm finds the best local optimum within a thousand iterations for most datasets, it repeatedly finds better local minima after several thousand iterations for some other datasets. For the latter case, Cyclic Exchange also finds better solutions than Lloyd's algorihtm. Although we are unable to identify the features that lead Cyclic Exchange to perform better, our results verify the robustness of Lloyd's algorithm in most datasets.
by Nattavude Thirathon.
M.Eng.
152 p.
6387181 bytes
6407754 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Cyclic exchange neighborhood search technique for the K-means clustering problem
Thesis
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<Abstract>Cyclic Exchange is an application of the cyclic transfers neighborhood search technique for the k-means clustering problem. Neighbors of a feasible solution are obtained by moving points between clusters in a cycle. This method attempts to improve local minima obtained by the well-known Lloyd's algorithm. Although the results did not establish usefulness of Cyclic Exchange, our experiments reveal some insights on the k-means clustering and Lloyd's algorithm. While Lloyd's algorithm finds the best local optimum within a thousand iterations for most datasets, it repeatedly finds better local minima after several thousand iterations for some other datasets. For the latter case, Cyclic Exchange also finds better solutions than Lloyd's algorihtm. Although we are unable to identify the features that lead Cyclic Exchange to perform better, our results verify the robustness of Lloyd's algorithm in most datasets.</Abstract>
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